Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.
Cite
CITATION STYLE
Bianchi, F., Nozza, D., & Hovy, D. (2022). XLM-EMO: Multilingual Emotion Prediction in Social Media Text. In WASSA 2022 - 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop (pp. 195–203). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.wassa-1.18
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